Then again - let's say I'm a user of some library and I trust the developers to do the right thing and test the new implementation against the old one. The thing that is of interest to me is whether on average on my random test I still see the difference. He did. Sure - the whole test is flawed, may be incorrect, etc. In reality - he just verified something he already knew/expected against his random data. No matter how much we care about correctness, he's still right on some level. There's a visible improvement even on this random test and if he tried it at least 3 times we can assume that the new GC is worth looking at if you had issues with the previous one.
"if it's not done well, it's not worth doing"? I'd say - if it answers your question, it's worth doing. If you assume the question was - "can I verify that the new GC is better in my random not-scientific test on a specific dataset", then everything's all right.
And I do hope that the person writing the new GC will publish some better stats.